Dynamic filtering. Instead of observing static receptive fields, several studies modified standard convolution to choose the adaptive receptive field for each pixel [1]}, [2]}, [3]}. SAC [4]} was proposed to modify the fixed-size receptive field by learning position-adaptive scale coefficients. Deformable ConvNets [5]}, [6]} learned offsets for each pixel in a regular sampling grid of standard convolution to enlarge the sampling field with an arbitrary form that can discover geometric-invariant features. Recently, [7]} proposed a dynamic convolution composed of multiple kernels with the same size to increase model complexity without increasing the network depth or width. All these existing works learn dynamic filters directly from the input images. They are suitable for segmentation or recognition tasks. For MVS task, they ignore the epipolar constraint between reference and source images. In contrast, our dynamic filtering is guided by normal curvature computed in the direction of epipolar line, which can measure the matching capacity between reference and source image patches. To the best of our knowledge, this is the first work that introduces dynamic filters to MVS.
